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Analyze racial differences in comfort with medical research using an alternative to the traditional approach that treats white people as a raceless norm.Quantitative analysis of survey responses (n = 1,570) from Black and white residents of the US to identify relationships between perceptions of research as a right or a risk, and comfort participating in medical research.A lower proportion of white respondents reported that medical experimentation occurred without patient consent (p 0.001) and a higher proportion of white respondents reported that it should be their right to participate in medical research (p = 0.02). Belief in one's right to participate was significantly predictive of comfort (b = 0.37, p 0.001). Belief in experimentation without consent was significantly predictive of comfort for white respondents but not for Black respondents in multivariable analysis.A rights-based orientation and less concern about the risks of medical research among white respondents demonstrate comparative advantage. Efforts to diversify medical research may perpetuate structural racism if they do not (1) critically engage with whiteness and its role in comfort with participation, and (2) identify and respond specifically to the needs of Black patients.

This study describes reported interest in notification regarding use of personal health information and biospecimens for research and preference-associated factors among a sample of the US population in 2019.

As predictive analytics are increasingly used and developed by health care systems, recognition of the threat posed by bias has grown along with concerns about how providers can make informed decisions related to predictive models. To facilitate informed decision-making around the use of these models and limit the reification of bias, this study aimed to (1) identify user requirements for informed decision-making and utilization of predictive models and (2) anticipate and reflect equity concerns in the information provided about models.Qualitative analysis of user-centered design (n = 46) and expert interviews (n = 10).We conducted a user-centered design study at an academic medical center with clinicians and stakeholders to identify informational elements required for decision-making related to predictive models with a product information label prototype. We also conducted equity-focused interviews with experts to extend the user design study and anticipate the ways in which models could interact with or reflect structural inequity.Four key informational elements were reported as necessary for informed decision-making and confidence in the use of predictive models: information on (1) model developers and users, (2) methodology, (3) peer review and model updates, and (4) population validation. In subsequent expert interviews, equity-related concerns included the purpose or application of a model and its relationship to structural inequity.Health systems should provide key information about predictive models to clinicians and other users to facilitate informed decision-making about the use of these models. Implementation efforts should also expand to routinely incorporate equity considerations from inception through the model development process.

Quality care requires collaborative communication, information exchange, and decision-making between patients and providers. Complete and accurate data about patients and from patients are especially important as high volumes of data are used to build clinical decision support tools and inform precision medicine initiatives. However, systematically missing data can bias these tools and threaten their effectiveness. Data completeness relies in many ways on patients being comfortable disclosing information to their providers without prohibitive concerns about security or privacy. Patients are likely to withhold information in the context of low trust relationships with providers, but it is unknown how experiences of discrimination in the healthcare system also relate to non-disclosure. In this study, we assess the relationship between withholding information from providers, experiences of discrimination, and multiple types of patient trust. Using a nationally representative sample of US adults (n = 2,029), weighted logistic regression modeling indicated a statistically significant relationship between experiences of discrimination and withholding information from providers (OR 3.7; CI [2.6-5.2], p .001). Low trust in provider disclosure of conflicts of interest and low trust in providers' responsible use of health information were also positively associated with non-disclosure. We further analyzed the relationship between non-disclosure and the five most common types of discrimination (e.g., discrimination based on race, education/income, weight, gender, and age). We observed that all five types were statistically significantly associated with non-disclosure (p .05). These results suggest that experiences of discrimination and specific types of low trust have a meaningful association with a patient's willingness to share information with their provider, with important implications for the quality of data available for medical decision-making and care. Because incomplete information can contribute to lower quality care, especially in the context of data-driven decision-making, patients experiencing discrimination may be further disadvantaged and harmed by systematic data missingness in their records.

This Viewpoint describes the decline in trust in medical institutions in the US and suggests approches to rebuilding and maintaining trust.

Objective To characterize challenges and strategies related to algorithmic risk scoring for care management eligibility determinations. Materials and Methods Interviews with 19 administrators from 13 physician organizations representing over 2200 physician offices and 8800 physicians in Michigan. Post-implementation interviews were coded using thematic analysis. Results Utility of algorithmic risk scores was limited due to outdated claims or incomplete information about patients’ socially situated risks (eg, caregiver turnover, social isolation). Resulting challenges included lack of physician engagement and inefficient use of staff time reviewing eligibility determinations. To address these challenges, risk scores were supplemented with physician knowledge and clinical data. Discussion and Conclusion Current approaches to risk scoring based on claims data for payer-led programs struggle to gain physician acceptance and support because of data limitations. To respond to these limitations, physician input regarding socially situated risk and utilization of more timely data may improve eligibility determinations.

Efforts to mitigate the spread of COVID-19 rely on trust in public health organizations and practices. These practices include contact tracing, which requires people to share personal information with public health organizations. The central role of trust in these practices has gained more attention during the pandemic, resurfacing endemic questions about public trust and potential racial trust disparities, especially as they relate to participation in public health efforts. Using an explanatory mixed methods design, we conducted quantitative analysis of state-level survey data in the United States from a representative sample of Michigan residents (n = 1000) in May 2020. We used unadjusted and adjusted linear regressions to examine differences in trust in public health information and willingness to participate in public health efforts by race. From July to September 2020, we conducted qualitative interviews (n = 26) to further explain quantitative results. Using unadjusted linear regression, we observed higher willingness to participate in COVID-19 public health efforts among Black survey respondents compared to White respondents. In adjusted analysis, that difference disappeared, yielding no statistically significant difference between Black and White respondents in either trust in public health information sources or willingness to participate. Qualitative interviews were conducted to explain these findings, considering their contrast with assumptions that Black people would exhibit lower trust in public health organizations during COVID-19. Altruism, risk acknowledgement, trust in public health organizations during COVID-19, and belief in efficacy of public health efforts contributed to willingness to participate in public health efforts among interviewees. Our findings underscore the contextual nature of trust, and the importance of this context when analyzing protective health behaviors among communities disproportionately affected by COVID-19. Assumptions about mistrust among Black individuals and communities may be inaccurate because they overlook the specific context of the public health crisis. These findings are important because they indicate that Black respondents are exhibiting strategic trust during COVID-19 despite systemic, contemporary, and historic barriers to trust. Conceptual specificity rather than blanket generalizations is warranted, especially given the harms of stereotyping and discrimination.

Key Points Question What are the national prevalence, frequency, and main types of discrimination that adult patients report experiencing in the US health care system? Findings In this nationally representative cross-sectional survey study, 21% of 2137 US adult survey respondents indicated that they had experienced discrimination in the health care system, and 72% of those who had experienced discrimination reported experiencing it more than once. Racial/ethnic discrimination was the most frequently reported type of discrimination respondents experienced. Meaning Experiences of discrimination in the health care system appear to be more common than previously recognized and deserve considerable attention., Importance Although considerable evidence exists on the association between negative health outcomes and daily experiences of discrimination, less is known about such experiences in the health care system at the national level. It is critically necessary to measure and address discrimination in the health care system to mitigate harm to patients and as part of the larger ongoing project of responding to health inequities. Objectives To (1) identify the national prevalence of patient-reported experiences of discrimination in the health care system, the frequency with which they occur, and the main types of discrimination experienced and (2) examine differences in the prevalence of discrimination across demographic groups. Design, Setting, and Participants This cross-sectional national survey fielded online in May 2019 used a general population sample from the National Opinion Research Center’s AmeriSpeak Panel. Surveys were sent to 3253 US adults aged 21 years or older, including oversamples of African American respondents, Hispanic respondents, and respondents with annual household incomes below 200% of the federal poverty level. Main Outcomes and Measures Analyses drew on 3 survey items measuring patient-reported experiences of discrimination, the primary types of discrimination experienced, the frequency with which they occurred, and the demographic and health-related characteristics of the respondents. Weighted bivariable and multivariable logistic regressions were conducted to assess associations between experiences of discrimination and several demographic and health-related characteristics. Results Of 2137 US adult respondents who completed the survey (66.3% response rate; unweighted 51.0% female; mean [SD] age, 49.6 [16.3] years), 458 (21.4%) reported that they had experienced discrimination in the health care system. After applying weights to generate population-level estimates, most of those who had experienced discrimination (330 [72.0%]) reported experiencing it more than once. Of 458 reporting experiences of discrimination, racial/ethnic discrimination was the most common type (79 [17.3%]), followed by discrimination based on educational or income level (59 [12.9%]), weight (53 [11.6%]), sex (52 [11.4%]), and age (44 [9.6%]). In multivariable analysis, the odds of experiencing discrimination were higher for respondents who identified as female (odds ratio [OR], 1.88; 95% CI, 1.50-2.36) and lower for older respondents (OR, 0.98; 95% CI, 0.98-0.99), respondents earning at least $50 000 in annual household income (OR, 0.76; 95% CI, 0.60-0.95), and those reporting good (OR, 0.59; 95% CI, 0.46-0.75) or excellent (OR, 0.41; 95% CI, 0.31-0.56) health compared with poor or fair health. Conclusions and Relevance The results of this study suggest that experiences of discrimination in the health care system appear more common than previously recognized and deserve considerable attention. These findings contribute to understanding of the scale at which interpersonal discrimination occurs in the US health care system and provide crucial evidence for next steps in assessing the risks and consequences of such discrimination. The findings also point to a need for further analysis of how interpersonal discrimination interacts with structural inequities and social determinants of health to build effective responses., This cross-sectional study examines the responses to a recent National Opinion Research Center survey to assess the prevalence, frequency, and main types of discrimination experienced by adult patients in the US health care system.

Background Improving primary care for patients with chronic illness is critical to advancing healthcare quality and value. Yet, little is known about what strategies are successful in helping primary care practices deliver high-quality care for this population under value-based payment models. Methods Double-blind interviews in 14 primary care practices in the state of Michigan, stratified based on whether they did (n = 7) or did not (n = 7) demonstrate improvement in primary care outcomes for patients with at least one reported chronic disease between 2010 and 2013. All practices participate in a statewide pay-for-performance program run by a large commercial payer. Using an implementation science framework to identify leverage points for effecting organizational change, we sought to identify, describe and compare strategies among improving and non-improving practices across three domains: (1) organizational learning opportunities, (2) approaches to motivating staff, and (3) acquisition and use of resources. Results We identified 10 strategies; 6 were “differentiating” – that is, more prevalent among improving practices. These differentiating strategies included: (1) participation in learning collaboratives , (2) accessing payer tools to monitor quality performance, (3) framing pay-for-performance as a practice transformation opportunity, (4) reinvesting earned incentive money in equitable, practice-centric improvement, (5) employing a care manager, and (6) using available technical support from local hospitals and provider organizations to support performance improvement. Implementation of these strategies varied based on organizational context and relative strengths. Conclusions Practices that succeeded in improving care for chronic disease patients pursued a mix of strategies that helped meet immediate care delivery needs while also creating new adaptive structures and processes to better respond to changing pressures and demands. These findings help inform payers and primary care practices seeking evidence-based strategies to foster a stronger delivery system for patients with significant healthcare needs.

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Introduction The growth of data science has led to an explosion in new knowledge alongside various approaches to representing and sharing biomedical knowledge in computable form. These changes have not been matched by an understanding of what healthcare delivery organizations need to do to adapt and continuously deploy computable knowledge. It is therefore important to begin to conceptualize such changes in order to facilitate routine and systematic application of knowledge that improves the health of individuals and populations. Methods An AHRQ‐funded conference convened a group of experts from a range of fields to analyze the current state of knowledge management in healthcare delivery organizations and describe how it needs to evolve to enable computable knowledge management. Presentations and discussions were recorded and analyzed by the author team to identify foundational concepts and new domains of healthcare delivery organization knowledge management capabilities. Results Three foundational concepts include 1) the current state of knowledge management in healthcare delivery organizations relies on an outdated biomedical library model, and only a small number of organizations have developed enterprise‐scale knowledge management approaches that “push” knowledge in computable form to frontline decisions, 2) the concept of Learning Health Systems creates an imperative for scalable computable knowledge management approaches, and 3) the ability to represent data science discoveries in computable form that is FAIR (findable, accessible, interoperable, reusable) is fundamental to spread knowledge at scale. For healthcare delivery organizations to engage with computable knowledge management at scale, they will need new organizational capabilities across three domains: policies and processes, technology, and people. Examples of specific capabilities were developed. Conclusions Healthcare delivery organizations need to substantially scale up and retool their knowledge management approaches in order to benefit from computable biomedical knowledge.

Improving primary care for high-needs patients is critical to advancing healthcare quality and value. Yet, little is known about what strategies have been successful in helping practices make the n...

Primary care practices have been limited in their ability to leverage electronic health records (EHRs) and health information exchange (HIE) to improve care coordination, but will soon be incentivized to do so under proposed Stage 3 meaningful use criteria. We use mixed methods to understand how primary care practices manage, share and reconcile electronic patient information across care settings, and identify innovations in EHR design to support enhanced care coordination. Opportunities identified by practices focused on availability and usability of features that facilitate (1) generation of customized summary of care records, (2) team-based care approaches, and (3) management of the increased volume of electronic information generated and exchanged during care transitions. More broadly, vendors and policymakers need to continue to work together to improve interoperability as the key to effective care coordination. If these EHR innovations were widespread, the value of meeting the proposed Stage 3 care coordination criteria would be substantially enhanced.